No input/output examples at the start, which threw me off more than I expected.
Start by clarifying the problem: what is the objective (e.g., maximize value, minimize cost), what are the exact dependency constraints (e.g., item A requires item B), and whether dependencies can be cyclic. Then model it as a graph problem (e.g., directed graph of dependencies) and identify the appropriate algorithmic technique (e.g., topological sort + dynamic programming, or maximum weight closure). Finally, discuss trade-offs between optimality and complexity, and consider edge cases like cycles or conflicting dependencies.
Pro tip: Demonstrate adaptability by acknowledging that real-world dependencies may be incomplete or change, and propose a solution that can handle dynamic updates or partial information. Also, relate it to Coinbase's domain (e.g., transaction dependencies, fee optimization) to show business awareness.
Ask questions to understand the objective function, dependency semantics (e.g., AND/OR, mandatory/optional), and whether dependencies can form cycles. Confirm if the problem is static or dynamic.
Represent items as nodes and dependencies as directed edges. Identify if the problem reduces to a known graph problem like maximum weight closure, knapsack with dependencies, or topological ordering.
Select an algorithm based on problem structure: e.g., dynamic programming on DAGs, min-cut for maximum closure, or greedy with topological sort. Discuss time/space complexity and optimality.
Address cycles (detect and handle via SCC condensation), conflicting dependencies, and large input sizes. Consider approximations if exact solution is NP-hard.
Walk through a small example to verify correctness, and clearly explain your reasoning and trade-offs. Be open to alternative interpretations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.